1. Measurable Space and Integration
The [Real Analysis] series of posts is my memo on the lecture Real Analysis (Spring, 2021) by Prof. Insuk Seo. The lecture follows the table of contents of Real and Complex Analysis (3rd ed.) by Rudin, with minor changes in order.
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1. Brief Introduction to Nonparametric function estimation
The [Nonparametric] series of posts is my memo on the lecture Nonparametric Function Estimation (Spring, 2021) by Prof. Byeong U. Park. The lecture is mainly focused on kernel smoothing, while also briefly covers other nonparametric methods such as MARS.
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1. Overview of Supervised Learning
The [Statistical Learning] series of posts are my summary of The Elements of Statistical Learning (ESL) and a memo on the lecture Advanced Data Mining (Spring, 2021) by Prof. Yongdai Kim. Main goal of the lecture is to interpret classical machine learning models in terms of statistics and decision theoretic framework.
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Understanding ELMo
Word2Vec and FastText paved the way to quality word embedding by utilizing context information, either word-level or character-level. ELMo (embeddings from language model) improved upon those with not only single context, but with both character and word-level contexts by dedicated architecture for the tasks.
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Understanding Latent Dirichlet Allocation (5) Smooth LDA
From background to two inference processes, I covered all the important details of LDA so far. One thing left over is a difference between (basic) LDA and smooth LDA. Consider this last post as a cherry on top.
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